Context is now the product: Product leadership when software can build itself | Karri Saarinen

00:06All right, good afternoon everyone. Um, I'm here today to talk about great products, context, and the product work we do. But first, I want to talk about my summer. Um, I so I took a break this summer from paying attention to AI. I stopped following the news like what is the latest model?

00:24What can it do? what agents of do we have and what what are the new techniques we can use and when I was I was on this vacation I I thought like when I when I come back maybe I will feel left behind maybe things change a lot but it realized that when I came came back actually realized that nothing has changed much um there's new models there's new techniques there's new agents but I think like in the end that the making great products and building like increasing revenue or building the business like it's still hard so it made me wonder how much attention we're really paying to these tools.

00:59Like we are really watching the tools a lot all the time. There's always new things we can try and it's clear that these tools are getting better. They can do more. They can do it faster. They're more capable. But the I think more important question is like are we actually making better things with these tools?

01:17And are our teams getting better with these tools? And I I don't think that that answer is is clear clearly yes. Like if you look out there and like even in your inside your own companies, how do you even know like how are you making better things?

01:31Do you feel that way? Does your customers feel that way? So there's a lot of emphasis now on tool making and using tools and building systems. But I think like in the end like as a product people our job is to to make something good. And so in some ways like today I think there's been several talks about software factories and I think that the industry has been I think obsessed with this idea a long time like how do we scale and optimize the output our organizations do and I think it makes sense and it is it is important to to move fast and like do things but this started even before AI

02:10and I think before AI the the idea was that we need to hire more people we create very specialized roles so they work in their own ways and then we create because there's a lot of people we create more process and because we have process and a lot of people we actually don't even know anymore like what what people are doing or what they're supposed to do.

02:26So we run experiments. So experiments the data tell us like what what works and what doesn't and this is all to like scale like how do we get more output than like how do we get more um output from the organization but again like my philosophy as always it's more more output isn't better like I I think like what we want to build is like better experiences for the customers one of my messages today is that don't like you can build the software factories and you can make and you can automate things and that all makes sense but just like as an organization Don't become one.

02:57Don't become a software factory where you out like outsource all the work and thinking to somewhere else and like just focus on like how efficient can the output be because the output is not the product. Like customers are not buying the lines of code.

03:13They're not buying the experiments or they're not buying a lot of things. So in the end like if you want to sell something or make some good product you have to make something someone wants. When I think about making things I think about two things.

03:24So making products produces two things. The the product and the learning. Historically the building products or designing them it's always created this learning as well. Like when you are struggling like trying to think like what you should build, how you should build it, which way, what kind of shape does it take, you have all these questions you are asking and maybe you're answering them yourself.

03:50Maybe you go talk to someone, maybe you talk to the customers and try to find the answer. So the actual effort you put into building things, it's also like teaches you something about what the problem is, what the customers uh want or like what what is their view.

04:04So we're kind of like in this time now that there's there's a little bit this danger of losing that uh direct direct connection with the with the learning. And I think like if you think about any great company out there or companies that create great products, you probably respect their product, but you probably also respect their team.

04:25And I think like products are usually just like it's kind of obvious, but they're like they're produced by the team in the company. And the better the team, the better they understand the space they're in, the better skills or talents they have, the better taste they have, like the better they build things.

04:41And so like great companies they consistently be able to to build great things because they have this culture and context built around like how what how to think about things like what matters what doesn't matter. So this is like all the context around customers about the product space they're in the technologies they can use the judgment the team uses to make decisions the taste they have to actually find what is good and all the history of the different things they've tried or or what they decided.

05:09And the image here is just a illustration of this kind of like compounding learning. It's a Formula 1 steering wheel which started with very kind of traditional form. But over the decades the the the kind of like the the team learned to how to really make it better and make it something that makes sense for this use case.

05:30Like Formula 1 cars, they don't need a lot of turning radius. So you need a lot of like small movements and like controlling the car in a high speed. It's very fine movements and then that's why the the actual steering wheel ended up different than what we typically see in cars.

05:45The danger really now with the product organization is that the more we automate stuff um to AI to do and more we automate it or tell teams to use AI, you kind of like create this separation from execution and from the learning. It's not necessarily bad thing like it's what I'm calling this out is that if this is the case and this is happening in your company like how are you going to fix it like eventually if you don't learn from the work you do I think eventually you would lose the advantage of what you have currently in your in your space or in your company

06:19one way to think about it is like well what is good to automate maybe what what is what is good for people to do I think there's definitely things that are repeatable and maybe not something you learn a lot about like even At winner we do now how to fix bugs.

06:30We have a we use it the winner loop to investigate the bug. It will connect to data do and sentry and other tools. It will try to look into the codebase and and try to understand where the bug is coming from and then it will write a fix. So it saves the time for the engineers like they don't have to investigate the problems.

06:49They come and and verify the result and and sometimes they make little changes for those results. But so the idea is like how do you save time um now with these new new tools but without really like outsourcing everything. Um so where I think about like the team should spend more time in is in the customer space and the customer problems.

07:12From the beginning, we always told at of the beginning of the company, we we've been pushing the engineers and all the team members really go connect with the customers, join the Slack channels, join the shared Slack channels, answer their questions, ask them questions, and like kind of like stay close to the customer and kind of like build your customer intuition that way.

07:33And I think like now if engineering or other roles have some time savings I think there's areas they can do more in like they can spend more time with customers they can explore more things they can train their judgment or or make better quality uh work than they previously had time to.

07:54So the goal I think really for the product organization in the future is like how do you keep this learning loop happening like how do you have context and and and bring signals from that context and and bring those learnings into the whole team and the whole company and I think like actually now AI can be a great tool in this.

08:14So a lot of times people I think focus on what AI can do what it can execute but not what AI can teach you. So I think for example at linear what we do is that we use the AI to help us to build this context. So we collect all the customer information that we can.

08:32Um we have automations that pull uh customer feedback from sales calls and other other meetings uh as well as from support emails and and internal uh discussions and that's kind of like builds this like a place for the context and place for the the kind of like the customer context and then like some something I started you doing recently is that the problem previously I think in a product organization especially it's a large one a lot of people start being kind of away from the customer problems on the day-to-day problems because there's so much of it and and

09:07you don't have the time to follow up with everyone or look at every single email or every request. But what I started doing is that we we collect all this context, but then I sent this like a watchers like an agent to watch it and see tell me when when there's something interesting for me.

09:23So one of these things I set up is this daily briefing about AI workflows. what are the customers saying about their AI workflows? I want to learn more from it because I've seen a lot of companies do it very differently. Uh today we I think there's no clear best practices.

09:40Every company does their stuff a little bit differently. And so that's why I'm trying to like learn more that like what are all the ways people are using these tools? What what do they want from us and how can we help them better? So this can it's it doesn't take a lot of time for my day to just read this.

09:57It gets few bullet points usually but I get this like a daily briefing basically saying like the these are the customer what they're saying about the AI workflows. On the judgment side I wanted to highlight couple things we do. I think what I've also noticed that when companies start using AI more and agents more people start uh siloing themselves like a lot of people are working alone with their agents building things and I think it's efficient but then there's also the problems like we not learning from each other anymore um or sport as much so one of the practices we start

10:36doing is this quality Wednesday and this happened because we as We keep hiring people. We we notice that not everyone has the same standard or understanding what what the quality means. And so we task everyone to go every week, spend some time, look into the product, try to find one quality defect in the product and then fix it.

10:59It can be very small. It can be weird hover state like janky animation. It can be a copy error. It can be something else. So it doesn't mean that like it should be like a huge project. It it could be like a five minute fix. The bigger impact with this is not really that we do this fixes.

11:14We do like that too. But the bigger impact is that the team trains everyone trains themselves to look for this like they train their eye to notice the small mistakes and then because we do this in a meeting everyone shares their findings and their fixes.

11:30Everyone can learn from that and see how other people are finding these problems. And then the second thing we do for new features is this feature roast. Uh it's a little similar but a little different where it's this optional meeting. Anyone in a company can join the team who is building the feature hostess and then what they ask people to do is just critique the whole feature.

11:52Like you can be as nitpicky or confused or whatever you want. You just put your raw feedback and it's not personal. It's just like this this is how I see it. And the team can still like interact with the people and ask them questions if they don't understand something.

12:07Again, like we're trying to train people that what matters and like what do they we care about and also like kind of train um give the team who is building the feature a little more like realistic way to see what maybe how the users would think about the feature because a lot of people in a company this is the first time for them seeing the feature.

12:26So they if they are confused it's likely that the the users are confused too. And then like after that the lead can synthesize the feedback and and make group it and make some kind of actionable issues out of it and then they can like fix it.

12:39But like I think the important part is that these are done together and there's this like discussion that happens with these meetings and that that kind of like gives everyone a little more everyone can learn from each other and they notice like when you're building on your own product or your own feature you remember one of these discussions and you know that oh those that person always cares about the onboarding or they care about this animation so I must I should check into that and like remember to do it so I don't get that feedback.

13:10So my like really the ask is that we we we have this idea that well AI can do a lot of stuff and like let's make everything more efficient. Let's produce more. But if if AI is really efficient and it's saving time, then maybe there's an opportunity to spend more of that time to something else.

13:27Like rather than focusing on the execution, we could sort of start focusing on the customers, the explorations, the the critiques, the quality or the even the reflection of what we do. I think like in the future the product organization maybe that the work we do changes and we move away from the spending so much time on the execution but actually going a little more abstract and like thinking about the the actual activities and like how do we become better as a team and how we can become better as a as a product organization.

13:57what I said earlier that the the I think companies are often compound understanding of the work and the the problems they they uh solve. So I think there's an important aspect that you should have some kind of place where you put this stuff like there's some place that's available for learning about the customer context or learning about your product thinking or learning about anything else you're doing and then I think that it's important for the people but it's also important for the agents.

14:26So I think now a lot of times I can now ask the agents like what do we have about this or teach me something about the customers like what are they saying and what are they doing and and like what how is the team responding to that. I think there is a value of like creating this like a shared space where this context lives um around the customers and the product and the the work that is happening around the product.

14:48I will end end this with this that I think that the tools will keep improving. And I think we will keep watching the tools and I think we will keep automating more and more stuff. But I think my view is that we should automate more of the known things like the things that are repeatable and maybe are not that impactful.

15:03It's not something we can learn a lot from. My hope is that like we can spend more time like how do we build the understanding of the what we're doing in this company or in this product organization like how do we make sure that the team is learning from something and from someone from somewhere and not just executing the next prototype or the next idea or something because I think I we we at linear we don't run experiments we we use we tell people to use their intuition but I often tell people that intuition is not some like magical force that like and Star Wars or something

15:36that it's happens to you. It it is something that you have learned while working on something or listening to customers or something. Intuition is basically your training in your brain that you have. And so the more you can learn and and listen and and see this context, I think it will compound your personal understanding and then eventually compounds the whole team's understanding.

15:58And so in the end I like what what this talk was about is like context becomes the product. Then like it's it's more that we we have this obsession to look at what is the output the software or the code but I think in the end it's it's I I look the other direction it's like what is actually creating the software like who is making the decisions and that's the people are doing that and the better context those people have and like better understanding they have about the what they're supposed to do I think the products are better and so at linear I always thought that hiring people is the

16:32first step building great products and maybe it's kind of obvious but I think sometimes people just think that I I need to hire to do something I need to increase the output but what you're really like trying to do is think about like what is the trajectory this person can create for the company like what kind of ideas they have like do they have the right judgment or taste how can they use it in this organization so in the end I think like more of the product organizations maybe comes around the it becomes the context like more of the work is about managing this context like

17:03re learning from it finding ways to to team to come together and learn learn from it versus the actual execution of the code or or building the thing so that's what I mean like context becomes the product thank